US8040387B2 - Image processing apparatus, image processing program, image processing method, and electronic camera for correcting texture of image - Google Patents
Image processing apparatus, image processing program, image processing method, and electronic camera for correcting texture of image Download PDFInfo
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- US8040387B2 US8040387B2 US12/216,883 US21688308A US8040387B2 US 8040387 B2 US8040387 B2 US 8040387B2 US 21688308 A US21688308 A US 21688308A US 8040387 B2 US8040387 B2 US 8040387B2
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N1/00—Scanning, transmission or reproduction of documents or the like, e.g. facsimile transmission; Details thereof
- H04N1/46—Colour picture communication systems
- H04N1/56—Processing of colour picture signals
- H04N1/58—Edge or detail enhancement; Noise or error suppression, e.g. colour misregistration correction
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N1/00—Scanning, transmission or reproduction of documents or the like, e.g. facsimile transmission; Details thereof
- H04N1/46—Colour picture communication systems
- H04N1/56—Processing of colour picture signals
- H04N1/60—Colour correction or control
- H04N1/6027—Correction or control of colour gradation or colour contrast
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N23/00—Cameras or camera modules comprising electronic image sensors; Control thereof
- H04N23/80—Camera processing pipelines; Components thereof
- H04N23/84—Camera processing pipelines; Components thereof for processing colour signals
- H04N23/88—Camera processing pipelines; Components thereof for processing colour signals for colour balance, e.g. white-balance circuits or colour temperature control
Definitions
- the present application relates to an image processing apparatus, an image processing program, an image processing method, and an electronic camera.
- a proposition is to provide an image processing technique for correcting texture of an image while preventing worsening of a noisy impression.
- An image processing apparatus includes a brightness fluctuation extraction section, a color difference fluctuation extraction section, a conversion section, and an addition section.
- the brightness fluctuation extraction section extracts a local fluctuation component from brightness information (hereinafter, a brightness fluctuation) in the image data.
- the color difference fluctuation extraction section extracts a local fluctuation component from color difference information (hereinafter, a color difference fluctuation) in the image data.
- the conversion section weightedly adds the color difference fluctuation and the brightness fluctuation to generate a pseudo texture component.
- the addition section adds the generated texture component to the brightness information.
- the conversion section adjusts and changes a weighting ratio of the weighted addition so as to make ratios of noises of color components included in the texture component substantially equal.
- the conversion section obtains, as information, white balance coefficients used when the image data is generated, and calculates the weighting ratio for making the ratios of color noises correlating with the white balance coefficients equal.
- the conversion section locally calculates the weighting ratio for making the ratios of level-dependent color noises included in the texture component equal.
- the conversion section obtains, as information, color correction coefficients of the image data, and calculates the weighting ratio for making the ratios of color noises correlating with the color correction coefficients equal.
- the conversion section calculates the weighting ratio for making the ratios of color noises correlating with the level difference equal.
- the addition section obtains, as information, a tone correction coefficient of the image data, and changes an addition coefficient of the texture component based on the tone correction coefficient.
- An image processing program is a program for causing a computer to function as the image processing apparatus according to any one of the above sections.
- An image processing method is a method of applying image processing to image data including brightness information and color difference information, the method including the following operations.
- a brightness fluctuation extraction operation . . . To extract a local fluctuation component from the brightness information (hereinafter, a brightness fluctuation) in the image data.
- a color difference fluctuation extraction operation . . . To extract a local fluctuation component from the color difference information (hereinafter, a color difference fluctuation) in the image data.
- a conversion operation . . . To weightedly add the color difference fluctuation and the brightness fluctuation to generate a pseudo texture component.
- the above conversion operation adjusts and changes a weighting ratio of the weighted addition so as to make ratios of noises of color components included in the texture component substantially equal.
- An electronic camera includes the image processing apparatus according to any one of the above sections, and an imaging section picking up an image of a subject to generate image data.
- the image processing apparatus applies image processing to the image data generated by the imaging section.
- the local fluctuation of the color difference information is reflected in the texture component of the brightness information, whereby a texture expression of the brightness information is enriched. Further, the weighting ratio for the color difference fluctuation is adjusted and changed to make the ratios of the color noises included in the texture component substantially equal. As a result, it is possible to prevent a noise of a specific color component from mixing in the texture component, which makes it possible to prevent a noise of the brightness information from worsening after the texture correction.
- FIG. 1 is a block diagram showing an image processing apparatus 51 .
- FIG. 2 is a flowchart to explain texture correction processing by the image processing apparatus 51 .
- FIG. 3A is an example of the image data as the processing target.
- FIG. 3B is view showing an example of the smoothed image.
- FIG. 3C and FIG. 3D are images to which texture emphasis are applied.
- FIG. 3E is an image resulting from the processing in the first embodiment.
- FIG. 4 is a block diagram showing the structure of an electronic camera 11 .
- FIG. 1 is a block diagram showing an image processing apparatus 51 of this embodiment.
- the image processing apparatus 51 includes a fluctuation extraction section 52 .
- the fluctuation extraction section 52 obtains brightness information and color difference information in image data to generate a brightness fluctuation and color difference fluctuations.
- the conversion section 54 weightedly adds the color difference fluctuations to the brightness fluctuation to generate a pseudo texture component of brightness.
- An addition section 55 applies texture correction to the brightness information by using the generated texture component.
- these constituent elements may be realized as software by a computer executing an image processing program.
- the above-described constituent elements may be realized as hardware by arithmetic circuits or the like.
- FIG. 2 is a flowchart to explain texture correction processing by the image processing apparatus 51 .
- Operation S 1 The fluctuation extraction section 52 obtains image data as a processing target.
- FIG. 3 [A] is an example of the image data as the processing target.
- Each pixel of the image data here has signal components including brightness information Y and color difference information Cb, Cr.
- Operation S 2 The fluctuation extraction section 52 smoothes each of the brightness information Y and the color difference information Cb, Cr in the image data by using an ⁇ filter or the like to obtain a smoothed image.
- FIG. 3 [B] is a view showing an example of the smoothed image.
- Operation S 3 The fluctuation extraction section 52 calculates a pixel difference between the brightness information Y in the image data and brightness information having undergone the smoothing to find a local fluctuation of the brightness information, that is, a brightness fluctuation ⁇ Y. Further, the fluctuation extraction section 52 calculates a pixel difference between the color difference information Cb, Cr in the image data and color difference information having undergone the smoothing to find local fluctuations of the color difference information, that is, color difference fluctuations ⁇ Cb, ⁇ Cr.
- the conversion section 54 In preparation for the weighted addition, the conversion section 54 properly decides weighting ratios ⁇ , ⁇ for the color difference fluctuations.
- a user can select one of decision methods of Operations S 5 ⁇ S 8 as a method for deciding the weighted ratios ⁇ , ⁇ .
- Operation S 5 The conversion section 54 obtains, as information, white balance coefficients (a gain Wr of the R component, a gain Wb of the B component) used when the image data is generated, from appended information or the like of the image data.
- white balance coefficients a gain Wr of the R component, a gain Wb of the B component
- an output level ratio of the RGB color components of an imaging device greatly varies depending on light source color temperature of the image data.
- a white balance coefficient for a color component is larger, its color signal is output from the imaging device with a smaller level and with a lower S/N.
- a noise ratio ⁇ R: ⁇ G: ⁇ B among the color components included in the image data is substantially equal to a white balance coefficient ratio Wr:1:Wb.
- ⁇ R: ⁇ G: ⁇ B Wr:1:Wb [3]
- the texture component D can be expressed as follows by using the fluctuations ⁇ R, ⁇ G, ⁇ B of the color components.
- the conversion section 54 calculates the solutions of the equations [6] based on the white balance coefficients to decide the weighting ratios ⁇ , ⁇ . After this decision, the conversion section 54 shifts its operation to Operation S 9 .
- Operation S 6 The conversion section 54 inverse-transforms the signal components YCbCr of each pixel of the image data to restore values with which the color components RGB are thought to have generated in the imaging device. By locally smoothing the values of the color components RGB, it is possible to calculate local signal levels of the color components RGB.
- the ratios of the color noises included in the texture component D also vary depending on the level-dependent noises.
- the conversion section 54 calculates the ratio ⁇ R: ⁇ G: ⁇ B of the level-dependent noises of the color components.
- the solutions ⁇ , ⁇ of the equations [8] are weighting ratios for making the ratios of the shot noises of the color components included in the texture component equal.
- the conversion section 54 decides the weighting ratios ⁇ , ⁇ for each local area of the image data. After this decision, the conversion section 54 shifts its operation to Operation S 9 .
- Operation S 7 The conversion section 54 obtains, as information, color correction coefficients of the image data from appended information or the like of the image data.
- the noise ratio ⁇ R: ⁇ G: ⁇ B among the color components included in the image data is expressed as follows, where Sr, Sg, and Sb are the color correction coefficients of the respective color components RGB.
- ⁇ R: ⁇ G: ⁇ B Sr:Sg:Sb [9]
- the solutions ⁇ , ⁇ of the equations [10] are weighting ratios for making changes in the color noise ratios in the texture component D accompanying the saturation correction equal.
- the conversion section 54 decides the weighting ratios ⁇ , ⁇ based on the color correction coefficients. After this decision, the conversion section 54 shifts its operation to Operation S 9 .
- Operation S 8 The conversion section 54 inverse-transforms the signal components YCbCr of each pixel in the image data by using the inverse transformation of the equation [1] to obtain values of the color components RGB. By locally smoothing the values of the color components RGB, it is possible to calculate local signal levels of the color components RGB. The ratios of the color noises included in the texture component D also vary depending on level differences among the color components.
- the signal level of the color component B becomes especially high.
- the color component B includes almost all the texture information of the blue area.
- the color component R includes little significant texture information and is mostly occupied by noises.
- the ratios of the color noises included in the texture component D vary depending on the level differences among the color components.
- the weighting ratios are adjusted so that a color component with a lower signal level has a less contribution ratio to the texture component D.
- the color noise ratio ⁇ R: ⁇ G: ⁇ B correlating with the level difference can be expressed by the following equation, where RGB are the local signal levels of the color components.
- ⁇ R: ⁇ G: ⁇ B GB:RB:RG [11]
- noise ⁇ R>> ⁇ B is estimated from the level difference R ⁇ B.
- the solutions ⁇ , ⁇ of the equations [12] are weighting ratios ⁇ , ⁇ in which the level differences among the color components are taken into consideration.
- the conversion section 54 calculates the solutions ⁇ , ⁇ for each area of the image data. After calculating all the weighting ratios ⁇ , ⁇ for the respective local areas, the conversion section 54 shifts its operation to Operation S 9 .
- Operation S 10 The addition section 55 adds the texture component D multiplied by a predetermined addition coefficient to the brightness information resulting from the smoothing (or the brightness information Y of the image data).
- the brightness fluctuation ⁇ Y has been amplitude-modulated in proportion to a tone conversion coefficient (gradient of a tone conversion curve) of the brightness information Y. Therefore, if the texture component D multiplied by a fixed addition coefficient is added to the brightness information, excessive texture and noise tend to occur in an image area with a large tone conversion coefficient. Therefore, by adjusting, locally or on a per pixel basis, the addition coefficient in substantially inverse proportion to the tone conversion coefficient, the addition of the texture component D can be performed properly.
- ⁇ Y brightness fluctuation
- image noise prominently increases, resulting in an image having a noisy impression.
- the texture after the correction becomes richer than that in FIG. 3 [C] by a degree corresponding to the texture emphasis using the color difference fluctuations.
- a specific color noise is mixed in the brightness information due to the texture correction, a noisy impression is given to this image, though not so great as that in FIG. 3 [C].
- the ratio of a noise of R (red) included in the texture component D is moderately reduced, a noisy impression in a fabric portion (blue) is successfully reduced.
- FIG. 4 is a block diagram showing the structure of an electronic camera 11 .
- an imaging lens 12 is mounted in the electronic camera 11 .
- a light-receiving surface of an imaging device 13 is disposed in an image space of the imaging lens 12 .
- the operation of the imaging device 13 is controlled by an output pulse of a timing generator 22 b.
- An image generated by the imaging device 13 is temporarily stored in a buffer memory 17 via an A/D conversion section 15 and a signal processing section 16 .
- the buffer memory 17 is coupled to a bus 18 .
- an image processing section 19 To the bus 18 , an image processing section 19 , a card interface 20 , a microprocessor 22 , a compression/expansion section 23 , an image display section 24 are coupled.
- the card interface 20 reads/writes data to/from a removable memory card 21 .
- a signal corresponding to a user's operation is input to the microprocessor 22 from a switch group 22 a of the electronic camera 11 .
- the image display section 24 displays an image on a monitor screen 25 provided on a rear surface of the electronic camera 11 .
- the texture correction of the first embodiment is executed by the microprocessor 22 and the image processing section 19 .
- This texture correction may be applied to image data immediately after the shooting or may be applied later to the image data recorded in the memory card 21 .
- an image processing server on the Internet may provide, as service, the image processing method for the texture correction to image data transmitted from a user.
- the texture correction is executed in the YCbCr color space.
- the present embodiment is not limited to this.
- the similar texture correction may be executed in the Lab color space, the HSV color space, or other color space.
- the texture emphasis is applied to the whole screen.
- the texture emphasis may be applied only to part of a screen (a main subject, a figure or skin-color area, a shaded part, a trimming range, a background part except a figure and skin-color area, and the like). In this case, it is possible to effectively emphasize the texture of a specific part in the screen while avoiding an adverse effect of noise increase on the whole screen.
- the embodiment is not limited to this.
- the smoothed color difference information may be obtained by subtracting the color difference fluctuation from the original color difference information. Further, for example, by subtracting the brightness fluctuation after adding the texture component to the original brightness information, it is possible to obtain brightness information with corrected texture.
- one of Operations S 5 ⁇ S 8 is selectively executed to decide the weighting ratios ⁇ , ⁇ .
- the embodiment is not limited to this.
- a plurality of operations out of Operations S 5 ⁇ S 8 may be executed to decide a plurality of kinds of weighting ratios.
Abstract
Description
Y=+0.2990R+0.5780G+0.1140B
Cb=−0.1687R−0.3313G+0.5000B
Cr=+0.5000R−0.4187G−0.0817B [1]
Texture component D=δY+αδCb+βδCr [2]
δR:δG:δB=Wr:1:Wb [3]
LδR:MδG:NδB=1:1:1 [5]
(0.3313t1−0.1687)α+(0.4187t1+0.5)β=0.587t1−0.299
(0.3313t2+0.5)α+(0.4187t2−0.813)β=0.587t2−0.114 [6]
where t1=1/Wr and t2=1/Wb.
δR:δG:δB=√R:√G:√B [7]
(0.3313t1−0.1687)α+(0.4187t1+0.5)β=0.587t1−0.299
(0.3313t2+0.5)α+(0.4187t2−0.813)β=0.587t2−0.114 [8]
where t1=√(G/R), t2=√(G/B).
δR:δG:δB=Sr:Sg:Sb [9]
(0.3313t1−0.1687)α+(0.4187t1+0.5)β=0.587t1−0.299
(0.3313t2+0.5)α+(0.4187t2−0.813)β=0.587t2−0.114 [10]
where t1=Sg/Sr and t2=Sg/Sb. Note that lower limit values of the values of Sr, Sg, Sb are limited so as not to become zero.
δR:δG:δB=GB:RB:RG [11]
(0.3313t1−0.1687)α+(0.4187t1+0.5)β=0.587t1−0.299
(0.3313t2+0.5)α+(0.4187t2−0.813)β=0.587t2−0.114 [12]
where t1=R/G and t2=B/G. Note that lower limit values of R, G, and B are limited so as not to become zero.
Texture component D=δY+αδCb+βδCr [2]
Claims (10)
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PCT/JP2007/000126 WO2007097125A1 (en) | 2006-02-27 | 2007-02-26 | Image processing device for correcting image massive feeling, image processing program, image processing method, and electronic camera |
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WO2009081485A1 (en) | 2007-12-25 | 2009-07-02 | Fujitsu Limited | Image processing apparatus, image processing method and image processing program |
JP5810593B2 (en) * | 2011-04-08 | 2015-11-11 | 株式会社ニコン | Image processing apparatus, imaging apparatus, and program |
CN108881875B (en) * | 2018-08-16 | 2020-01-14 | Oppo广东移动通信有限公司 | Image white balance processing method and device, storage medium and terminal |
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US20150071562A1 (en) * | 2013-09-11 | 2015-03-12 | Ricoh Company, Limited. | Image processing apparatus |
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JP4752912B2 (en) | 2011-08-17 |
WO2007097125A1 (en) | 2007-08-30 |
US20090046166A1 (en) | 2009-02-19 |
JPWO2007097125A1 (en) | 2009-07-09 |
EP1991008A1 (en) | 2008-11-12 |
EP1991008A4 (en) | 2012-04-18 |
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